research-article

SigmaKB: multiple probabilistic knowledge base fusion

Authors:

Miguel Rodríguez,

Sean Goldberg,

Daisy Zhe WangAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 9, Issue 13

Pages 1577 - 1580

https://doi.org/10.14778/3007263.3007313

Published: 01 September 2016 Publication History

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Abstract

The interest in integrating web-scale knowledge bases (KBs) has intensified in the last several years. Research has focused on knowledge base completion between two KBs with complementary information, lacking any notion of uncertainty or method of handling conflicting information. We present SigmaKB, a knowledge base system that utilizes Consensus Maximization Fusion and user feedback to integrate and improve the query results of a total of 71 KBs. This paper presents the architecture and demonstration details.

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Dellal IJean SHadjali AChardin BBaron M(2019)Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query resultsKnowledge and Information Systems10.1007/s10115-019-01332-761:3(1633-1665)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10115-019-01332-7

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 9, Issue 13

September 2016

378 pages

ISSN:2150-8097

Editor:
Surajit Chaudhuri
Microsoft Research

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VLDB Endowment

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Published: 01 September 2016

Published in PVLDB Volume 9, Issue 13

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Dellal IJean SHadjali AChardin BBaron M(2019)Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query resultsKnowledge and Information Systems10.1007/s10115-019-01332-761:3(1633-1665)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10115-019-01332-7

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Knowledge acquisition for fuzzy knowledge base development in surface mount pwb assembly

Embedable Problem-Solving Architectures: A Study of Integrating OPS5 with UMass GBB

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